Hierarchical Tree Indexing for Biological Experiment Data
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Solution Overview
Problem
Current database management systems face challenges in efficiently storing, identifying, and retrieving biological experiment data due to lack of standardization, leading to suboptimal storage and retrieval processes, and poor user interaction experiences.
Innovation Solution
A records management system generates system identifiers with segmented attributes for experiment data, allowing for efficient indexing and visualization as a dynamic tree, enabling instantaneous data insertion, search, and retrieval, while maintaining data from different vendors without normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional DBMS indexing approaches are used, then data storage and retrieval can be performed, but data accessibility and usability deteriorate due to arbitrary or opaque indices that are not readily understood by users
Solution Approach 1:
The patent segments the database index into a hierarchical tree structure with multiple levels, where each level represents a different attribute dimension (e.g., subject, sample, aliquot, experiment). This segmentation makes the index more interpretable for users while maintaining efficient data retrieval, directly resolving the contradiction between ease of operation and information loss.
Solution Approach 2:
The patent transitions from traditional flat table indexing to a multi-dimensional hierarchical tree structure. This dimensional change allows the system to organize data across multiple levels of abstraction, making the index both efficient for retrieval and intuitive for users to navigate and understand.
2Adaptability or versatility
If datasets from multiple vendors are stored without standardization, then data diversity is maintained, but database function performance deteriorates due to lack of uniform format
Solution Approach 1:
The patent implements a universal hierarchical tree structure that can accommodate data from multiple vendors and formats. The tree's flexible node structure allows it to represent different data schemas while maintaining consistent navigation and query operations, enabling the database to handle diverse data without sacrificing operational efficiency.
Solution Approach 2:
The hierarchical tree structure acts as an intermediary layer between the raw vendor-specific data formats and the database operations. It translates and normalizes data from various formats into a unified structure without requiring changes to the original data or the database engine, thus maintaining both diversity and efficiency.
3Quantity of substance
If multiple datasets from a single subject are stored with similar metadata, then data completeness is improved, but retrieval efficiency deteriorates due to concentration of locations in one section of the database
Solution Approach 1:
The patent segments the database into a hierarchical tree structure where data from the same subject are distributed across different branches rather than concentrated in one section. This segmentation maintains data completeness while improving retrieval efficiency by enabling targeted navigation through the tree structure.
Solution Approach 2:
The patent implements a dynamic tree structure that can adapt to the organization of data based on their metadata attributes. This dynamic organization allows efficient retrieval by navigating the tree according to the specific query criteria, rather than searching through a static, concentrated data layout.
4Manufacturing precision
If normalization is performed on datasets before storage, then data standardization is improved, but processing time increases due to the normalization requirement
Solution Approach 1:
The patent performs preliminary organization of data into the hierarchical tree structure at the time of insertion, rather than performing normalization as a separate subsequent step. This preliminary action integrates standardization with storage, eliminating the time penalty associated with separate normalization operations while maintaining data standardization.
Data Source
AI summary
The present disclosure is directed to managing and accessing experiment data. A data processing system may generate a system identifier for each instance that leads to a generation of experiment data. A first segment may refer to a source entity, and may be used to differentiate identifiers generated by different source entities. This implementation may aim to provide an explicit representation of the data generation process while providing a glimpse on select metadata attributes, such as an individual species, sample class, and experimental technique used to generate the dataset. These constraints may prevent duplicates and may enable system-side associations among subjects, samples, aliquots, and experiments.